I am trying to achieve a script to execute sequence-to-label(response) classification.
I am having a problem with how the function trainNetwork wants the input data packaged.
I am trying to figure out what to do with XTrain or XTrainTest, to get the 'trainNetwork' command to accept the input data.
I am receiving the following error: 'Error using trainNetwork
Invalid training data. Predictors must be a numeric array, a datastore, or a table. For networks with sequence input, predictors can also be a cell array of sequences. '
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clear all;
close all;
clc
%--------------------------------------------------------------------------
% Generating A matrix
NewDimension = uint16(200);
mu = 5;
std = 2.1;
A = normrnd(mu, std, [NewDimension 784] );
%--------------------------------------------------------------------------
N_sample = 60000;
N_test=10000;
%XTrain = zeros(28,28,1,N_sample);
XTrain = zeros(1,NewDimension,1,N_sample);
XTrainTest = cell(N_sample,1);
YTrain=zeros(N_sample,1);
% Please dowload the MNIST data set from http://yann.lecun.com/exdb/mnist/
% and unzip.
fidimg1=fopen('train-images.idx3-ubyte','rb');
fidimg2=fopen('train-labels.idx1-ubyte','rb');
[img,count]=fread(fidimg1,16); % table head
[imgInd,count1]=fread(fidimg2,8); %table head
for k=1:N_sample
[im,~]=fread(fidimg1,[28,28]);
ind=fread(fidimg2,1);
clear testsave, clear testmult
testsave = reshape(im, 784, 1);
testmult = A*testsave;
XTrain(1,1:NewDimension,1,k)=transpose(testmult);
XTrainTest{k} = testmult';
%XTrainTest{k} = double(transpose(testmult));
%XTrain(:,:,1,k)=im';
YTrain(k)=ind;
end
fclose(fidimg1);
fclose(fidimg2);
YTrain=categorical(YTrain);
%-------------------------------------------------------------------------
XTest = zeros(1,NewDimension,N_test);
%XTest = zeros(1,784,1,N_test);
YTest=zeros(N_test,1);
fidimg1=fopen('t10k-images.idx3-ubyte','rb');
fidimg2=fopen('t10k-labels.idx1-ubyte','rb');
[img,count]=fread(fidimg1,16);
[imgInd,count1]=fread(fidimg2,8);
for k=1:N_test
[im,~]=fread(fidimg1,[28,28]);
ind=fread(fidimg2,1);
clear testsave, clear testmult
testsave = reshape(im, 784, 1);
testmult = A*testsave;
XTest(1,1:NewDimension,k)=transpose(testmult);
%XTest(:,:,1,k)=im';% training set building
YTest(k)=ind;
end
fclose(fidimg1);
fclose(fidimg2);
YTest=categorical(YTest);
numHiddenUnits = 400;
numClasses = 10;
numFeatures = NewDimension;
% Modify XTrain and XTest to be 2-D arrays
XTrain = reshape(XTrain, [NewDimension, N_sample]);
%YTrain = transpose(YTrain);
%XTest = reshape(XTest, [NewDimension, N_test]);
layers = [
featureInputLayer(numFeatures)
fullyConnectedLayer(100) % number of neurons in the first hidden layer
batchNormalizationLayer
reluLayer
fullyConnectedLayer(50) % number of neurons in the second hidden layer
batchNormalizationLayer
reluLayer
fullyConnectedLayer(numClasses)
softmaxLayer
classificationLayer];
maxEpochs = 5;
miniBatchSize = 20;
options = trainingOptions('adam', ...
'ExecutionEnvironment','cpu', ...
'MaxEpochs',maxEpochs, ...
'MiniBatchSize',miniBatchSize, ...
'GradientThreshold',1, ...
'Verbose',false, ...
'Plots','training-progress');
net = trainNetwork(XTrainTest,YTrain,layers,options);
